Robust Face Recognition Against Eyeglasses Interference by Integrating Local and Global Facial Features

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 772)

Abstract

In this paper, we proposed a feature extraction method to solve a challenge problem of face recognition, i.e., recognition of faces with eyeglasses. By fusing the local and global facial features, the proposed method can extract robust facial features that can greatly reduce the negative influence of eyeglasses on face recognition. Firstly, we use the Ununiformed Local Gabor Binary Pattern Histogram Sequence (ULGBPHS) method to extract local facial features. Secondly, we apply 2D-Discrete Fourier Transform (2D-DFT) method to obtain global facial features. Finally, we use a weighted fusion strategy to combine the two kinds of facial features for face recognition. Extensive experimental results on the well-known public GT and CMU_PIE face datasets, and real scene dataset which is built by our group show that the proposed feature extraction method obtains the best performance among some state-of-the-art methods. The relevant code and data will be available at http://www.yongxu.org/lunwen.html.

Keywords

Face recognition Eyeglasses Local and global facial features 

Notes

Acknowledgments

This paper is partially supported by Guangdong Province high-level personnel of special support program (No. 2016TX03X164) and Shenzhen Fundamental Research fund (JCYJ20160331185006518).

References

  1. 1.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24670-1_36 CrossRefGoogle Scholar
  2. 2.
    Arya, S., Pratap, N., Bhatia, K.: Future of face recognition: a review. Procedia Comput. Sci. 58(2), 578–585 (2015)CrossRefGoogle Scholar
  3. 3.
    Coetzee, L., Botha, E.C.: Fingerprint recognition in low quality images. Pattern Recogn. 26(10), 1441–1460 (1993)CrossRefGoogle Scholar
  4. 4.
    Deng, Y., Guo, Z., Chen, Y.: Fusing local patterns of gabor and non-subsampled contourlet transform for face recognition. In: IAPR Asian Conference on Pattern Recognition, pp. 481–485 (2013)Google Scholar
  5. 5.
    Givens, G., Beveridge, J.R., Draper, B.A., Grother, P., Phillips, P.J.: How features of the human face affect recognition: a statistical comparison of three face recognition algorithms. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 381–389 (2004)Google Scholar
  6. 6.
    Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. Image Vis. Comput. 28(5), 807–813 (2010)CrossRefGoogle Scholar
  7. 7.
    Guo, K., Wu, S., Xu, Y.: Face recognition using both visible light image and near-infrared image and a deep network. CAAI Trans. Intell. Technol. 2(1), 39–47 (2017)CrossRefGoogle Scholar
  8. 8.
    Heo, J., Kong, S.G., Abidi, B.R., Abidi, M.A.: Fusion of visual and thermal signatures with eyeglass removal for robust face recognition. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2004, p. 122 (2004)Google Scholar
  9. 9.
    Kong, A., Zhang, D., Kamel, M.: A survey of palmprint recognition. Pattern Recogn. 42(7), 1408–1418 (2009)CrossRefGoogle Scholar
  10. 10.
    Liu, L., Sun, Y., Yin, B., Song, C.: A novel nonuniform division strategy for wearing eyeglasses face recognition. In: Fifth International Conference on Image and Graphics, pp. 907–911 (2009)Google Scholar
  11. 11.
    Martínez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 748–763 (2002)CrossRefGoogle Scholar
  12. 12.
    Nefian, A.: Georgia tech face database (2013), http://www.anefian.com/research/face_reco.html
  13. 13.
    Ojala, T., Harwood, I.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  14. 14.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefMATHGoogle Scholar
  15. 15.
    Solanki, K., Pittalia, P.: Review of face recognition techniques. Int. J. Comput. Appl. 133, 20–24 (2016)Google Scholar
  16. 16.
    Xu, Y., Fang, X., Li, X., Yang, J., You, J., Liu, H., Teng, S.: Data uncertainty in face recognition. IEEE Trans. Cybern. 44(10), 1950–1961 (2014)CrossRefGoogle Scholar
  17. 17.
    Xu, Y., Lu, Y.: Adaptive weighted fusion: a novel fusion approach for image classification. Neurocomputing 168, 566–574 (2015)CrossRefGoogle Scholar
  18. 18.
    Yang, P., Shan, S., Gao, W., Li, S.Z., Zhang, D.: Face recognition using Ada-boosted gabor features. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 356–361 (2004)Google Scholar
  19. 19.
    Yi, D., Li, S.Z.: Learning sparse feature for eyeglasses problem in face recognition. In: 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011), pp. 430–435. IEEE (2011)Google Scholar
  20. 20.
    Yu, S.U., Shan, S.G., Chen, X.L., Wen, G.: Integration of global and local feature for face recognition. J. Softw. 21(8), 1849–1862 (2010)CrossRefMATHGoogle Scholar
  21. 21.
    Zanchettin, C.: Face recognition based on global and local features. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 55–57. ACM (2014)Google Scholar
  22. 22.
    Zhang, W., Shan, S., Chen, X., Gao, W.: Local gabor binary patterns based on kullback-leibler divergence for partially occluded face recognition. IEEE Signal Process. Lett. 14(11), 875–878 (2007)CrossRefGoogle Scholar
  23. 23.
    Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In: Tenth IEEE International Conference on Computer Vision, pp. 786–791 Vol. 1 (2005)Google Scholar
  24. 24.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003), http://doi.acm.org/10.1145/954339.954342

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Bio-Computing Research Center, Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhenChina
  2. 2.Key Laboratory of Network Oriented Intelligent ComputationShenzhenChina
  3. 3.Medical Biometrics Perception and Analysis Engineering LaboratoryShenzhenChina

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